* Corresponding author: ani.suryani@um.ac.id
Stock Information on Social Media and Stock Return
ZHULVIRA SYAFITRI ANI WILUJENG SURYANI*
Universitas Negeri Malang, Indonesia
Abstract: Information on social media, both positive and negative, can spread quickly so that it can affect the company's stock price. This study aims to determine the effect of posting on Instagram on abnormal returns. The sample used is 2,675 posts from 18 stock news accounts. Generalized Least Squares (GLS) regression is used to determine the impact of posting on Instagram on abnormal returns. The results showed that sentiment on social media had a positive effect on abnormal returns. However, there is a possibility of misinterpretation by investors and the participation of impostors, so the impact of information on social media on abnormal returns is only temporary. This study contributes to the behavioral finance literature by examining the effect of sentiment on Instagram on abnormal returns. In addition, information on social media can be utilized by investors by selling their ownership when the stock gets positive sentiment to get abnormal returns. Investors can buy stocks when there is a negative sentiment on social media because the stock price is lower than its intrinsic value, so it has the potential to increase again.
Keywords: Social Media, Instagram, Sentiment, Abnormal Return
Abstrak— Informasi di media sosial baik itu positif maupun negatif dapat menyebar dengan mudah sehingga dapat memengaruhi harga saham perusahaan. Penelitian ini bertujuan untuk mengetahui pengaruh postingan di Instagram terhadap abnormal return. Sampel yang digunakan berupa 2.675 postingan dari 18 akun berita saham.
Regresi Generalized Least Squares (GLS) digunakan untuk mengetahui dampak postingan di Instagram terhadap abnormal return. Hasil penelitian menunjukkan bahwa sentimen di media sosial berpengaruh positif terhadap abnormal return. Akan tetapi, terdapat kemungkinan kesalahan penafsiran oleh investor dan keikutsertaan impostor sehingga dampak informasi di media sosial terhadap abnormal return hanya bersifat sementara. Penelitian ini berkontribusi terhadap literatur behavioral finance dengan meneliti pengaruh sentimen di Instagram terhadap abnormal return. Selain itu, informasi di media sosial dapat dimanfaatkan oleh investor dengan menjual kepemilikannya ketika saham mendapatkan sentimen positif guna mendapatkan abnormal return. Investor dapat membeli saham ketika terdapat sentimen negatif di media sosial karena harga saham menjadi lebih rendah dari nilai intrinsiknya sehingga berpotensi untuk kembali meningkat.
Kata Kunci: Media Sosial, Instagram, Sentimen, Abnormal Return
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1. Introduction
Information on social media is published by parties of various backgrounds and fields of expertise, which makes information filtering and verification challenging (Lei et al., 2019). Consequently, social media is prone to contain speculative and misleading information (Bartov et al., 2018). Positive and negative information on social media is readily available to spread, which may affect corporations’ stock prices (Jiao et al., 2020; Renault, 2017). A case in point is one in which the Associated Press’ Twitter account was hacked and made to release a false statement on a bombing event in the White House where President Obama was reportedly injured had caused a loss worth
$200 billion on the stock market (Bisnis Tempo, 2013). In another instance, Tesla also witnessed a drop down to $13 billion in market value following Elon Musk's tweet that Tesla's stock price was too high in his opinion (Sugiharto, 2021). The changes in stock prices in the abovementioned cases show that information over social media might influence stock returns.
The tweets by the Associated Press hacked account and by Elon Musk demonstrate that stock prices react instantaneously to new information, reflecting efficient market conditions (Singh et al., 2021). The efficient market hypothesis assumes that stock prices perfectly reflect existing information (Fama, 1970).
Therefore, investors process information rationally (Hirshleifer, 2015). On the other hand, behavioral finance states that investors’ irrational mood also influences stock prices (Kapoor & Prosad, 2017; McGurk et al., 2020). This concept assumes that investors cannot collect and process information quickly, leading to them not being perfectly rational in making decisions (Ahmad et al., 2017; Li et al., 2019). This behavioral factor that affects irrational actions can be seen in investors' overreaction to the information circulating on social media (Bukovina, 2015).
Prior research works have analyzed the effect of information on social media platforms, such as Facebook and Twitter, on stock prices (Agarwal et al., 2021; Bartov et al., 2018; Jiao et al., 2020; Karabulut, 2013; Siganos et al., 2014; Teti et al., 2019).
Those works discovered that positive information on Twitter was positively correlated with stock returns (Agarwal et al., 2021; Bartov et al., 2018; Fan et al., 2020b; Gu &
395 Kurov, 2020). This shows that the better the sentiments spread on social media, the higher the stock returns generated. This is because the information deriving from Twitter is issued not only by the company but also by all investors who share their perspectives on the company's prospects and stocks with each other (Bartov et al., 2018). Another work found that negative information on Facebook was negatively correlated with trade volume and market volatility (Siganos et al., 2014). This indicates that the worse the sentiments, the more frequently the investors would make transactions as they strove to generate better outcomes from other activities (Siganos et al., 2014). Thus, information on social media will potentially influence stock returns.
Research on social media and stock prices using the Instagram platform is scarce.
Therefore, this research offers novelty using Instagram, focusing on posts that can reach large audiences. Indonesians of productive age (16-64 years) are more likely to use Instagram than Tik Tok and Youtube (Date Reportal, 2022). Investors in this age bracket have assets exceeding Rp 500 trillion, or approximately 49 percent of total assets in the capital market (KSEI, 2022b). Moreover, 59% of total assets in the capital market are owned by local investors (KSEI, 2022b). With a user base of 92 million users in Indonesia (Rizaty, 2021), posts on Instagram can affect stock prices (Nyman
& Persson, 2021).
An instance is a drop in Bakrie Group's stock prices in the wake of news reporting on Nia Ramadhani and Ardi Bakrie's arrests for drug abuse first through the Instagram account @jktnewss (CNN Indonesia, 2021; Iswinarno & Yasir, 2021). This impact lasted up to three days after the post was uploaded (CNBC Indonesia, 2021c). This is because Ardi Bakrie is the Vice President Director of PT Visi Media Asia Tbk (VIVA) and Vice Executive Director of PT Bakrie & Brothers Tbk (BNBR) (CNBC Indonesia, 2021b). Negative news reporting on Ardi Bakrie affected the stock price of the companies. This research, therefore, attempted to determine the effect of stock-related news or information on Instagram on stock returns. This research contributes to the behavioral finance literature by investigating the effect of stock information over social media on stock returns.
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2. Theoretical Framework and Hypothesis Development
The efficient market hypothesis (EMH) states that stock prices are unpredictable as they already reflect all relevant information (Fama, 1970). The efficient market hypothesis assumes that investors can process information rationally (Hirshleifer, 2015). Therefore, they will make investment decisions based on data, risk and return assessment, and their knowledge and experiences in the capital market (Cohen &
Kudryavtsev, 2012; Nyamute et al., 2015; Prosad et al., 2015). As a result, stocks will react rapidly to new information (Latif et al., 2011; Prosad et al., 2015; Singh et al., 2021), such as information on dividend announcements, earnings growth, turnover, and expansion plan (Sharma & Kumar, 2020).
However, the efficient market hypothesis cannot explain what triggers market anomalies (Latif et al., 2011; Singh et al., 2021). A market anomaly is a situation where the performance of a stock deviates from the efficient market assumption (Latif et al., 2011) due to investors’ underreaction and overreaction (Fama, 1998). As such, behavioral finance emerges as an alternative perspective to analyze the causes of such anomalies (Prosad et al., 2015; Singh et al., 2018). Behavioral finance assumes that investors' irrational behaviors influence the stock market as they rely on sentiments and other non-fundamental information (Ge et al., 2020). This concept stresses the understanding of irrational investment decision-making by investors as a result of psychological factors (Kapoor & Prosad, 2017; Singh et al., 2018). Investors' irrational decision-making is driven by their incapability to collect and process information quickly (Li et al., 2019) and their tendency to fall under the influence of others' perspectives (Zahera & Bansal, 2018).
Irrational behaviors can be reflected in investors’ activities and interactions over social media (Bukovina, 2016). Social media is often used to collect information to lay the ground for investors' investment decision-making (Kadous & Mercer, 2017).
Company information on social media may come from two primary sources: the inside and outside of the company (Lei et al., 2019; Zhou et al., 2015). Information derived internally from the company tends to be biased, in which case only positive news or information is spread (Yang & Liu, 2017), potentially misleading the information users
397 (Lei et al., 2019). On the other hand, information that is externally derived may help users verify the information accessed from within the company while sharing their opinions with other users (Lei et al., 2019). However, external information is hard to filter and verify (Lei et al., 2019), leading to its speculative and misleading nature (Bartov et al., 2018). Information on the company on social media internally and externally derived, plays a significant role in shaping public opinions and influencing market responses (Bartov et al., 2018; Blankespoor, 2018; Bollen et al., 2011;
Kietzmann et al., 2011).
Earlier research has analyzed the effect of information over social media on stock returns (Agarwal et al., 2021; Bartov et al., 2018; Jiao et al., 2020; Karabulut, 2013;
Siganos et al., 2014; Teti et al., 2019). One of the media platforms frequently used to acquire financial information is Twitter (Gu & Kurov, 2020). Investors may exchange information regarding companies’ prospects and stocks over Twitter (Bartov et al., 2018). A previous study found that positive information on Twitter was positively correlated with the stock returns of companies listed on the Russell 3000 Index (Gu &
Kurov, 2020). This shows that the better the company’s information, the higher the stock returns generated. This is because positive information reflects the company’s prospects in the future (Gu & Kurov, 2020).
The effect of positive sentiments on Twitter on stock returns will last even longer if the information contained is fundamental, such as information on analysis recommendations, targeted changes in stock prices, and the company's revenue (Gu &
Kurov, 2020). Contrarily, if the positive information only reflects the sentiments of uninformed traders, such as unfounded rumors, then the effect on stock returns will be fleeting (Gu & Kurov, 2020). Another research work also discovered that positive information on Twitter, posted both by bots and authentically by the company, positively affected stock returns (Fan et al., 2020b). This shows that the higher the amount of positive information posted by the original account or bots, the higher the stock returns. However, bot-generated information may cause a high level of volatility due to the divergence in investors’ perspectives regarding the company’s value (Fan et al., 2020b).
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Earlier research also figured out that positive information on Twitter positively influenced the stock returns of companies listed on the S&P 500 Index (Sul et al., 2017). This indicates that the better the sentiments on social media, the greater the returns generated. Nonetheless, the information issued by accounts with large follower bases did not affect stock returns on the following trading day and vice versa (Sul et al., 2017). The explanation is that stock prices can promptly reflect the information posted by accounts with many followers (Sul et al., 2017). Meanwhile, the information posted by accounts with a small number of followers will spread slowly, significantly affecting the following trading day (Sul et al., 2017). A previous study also reported that negative information circulated over Facebook negatively correlated with trading volume and market volatility (Siganos et al., 2014). This shows that negative sentiments on social media would result in a greater frequency at which investors conduct stock trading in the hope that they would earn better outcomes from other activities (Siganos et al., 2014). Another study found that stock recommendations on Instagram could influence stock returns (Nyman & Persson, 2021).
The fact that stock prices change in response to information on social media shows that investors are not perfectly rational in making decisions as they are also influenced by psychological factors (Singh et al., 2018) and perceptions of others (Zahera &
Bansal, 2018), which reflects the behavioral finance concept. Therefore, the hypothesis of this research is as follows:
H1: Sentiments on social media positively affect abnormal returns.
3. Research Method
This research used the event study method to measure the effectiveness of an event on a company's stocks (Binder, 1998). The data used were Instagram posts of stock news accounts with followers of more than 100,000 or those that are called macro- influencers (Kay et al., 2020) which were selected due to their potential for reaching more audiences. The stock news accounts were collected using the Instagram search feature with the keywords "saham" and "stock". The following stock news accounts from this search were found: @ruangsaham, @sahamtalk, @bacasaham, @stockbit,
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@cuanderful, @faktatrading, @sahamology, @kuliahsaham, @ngertisaham,
@glimpse.inc, @emtrade_id, @sahamdaily, @gurusaham, @belajarsahamindonesia,
@sahamrakyat, @nasahind, @stockup_indonesia, and @indonesiastockexchange.
The population of this study consists of all posts of stock news accounts.
Through purposive sampling, we selected posts that provided information related to company stock and uploaded them in 2021. The research period was restricted to only 2021 as the number of investors on the capital market then increased by 92.99% from the previous year (KSEI, 2022a), and the number of stock news accounts also soared compared with preceding years1. The year 2022 was not selected because the stock trading volume in the first quarter was lower than in the first quarter of 2021 (IDX, 2022). Posts in charts without captions and reposts of other news stories or posts were excluded.
Table 1
Number of Instagram Posts
Criteria Number of Posts
Total posts 3.115
Posts with the same stock code and posting
date (225)
Number of posts used 2.890
The posts were then classified into posts with positive, neutral, and negative sentiments (Gu & Kurov, 2020). Posts with positive sentiments were praises, recommendations, feedback, and reflections of positive emotions such as satisfaction, pleasure, and happiness. Posts were classified as positive sentiments if positive sentences comprised 2/3 of the total sentences (Wei et al., 2017). Posts with positive sentiments would be coded 1. An example of a post with a positive sentiment is as follows: In June 2021, “X” managed to record a net profit of Rp156.06 billion, in
1 This information was obtained by comparing the number of stock news accounts in 2021 with 2020 seen from the year the Instagram account was created (Baca Saham, 2022; Belajar Saham Indonesia, 2022; Cuanderful, 2022; Emtrade, 2022; Fakta Trading, 2022; Glimpse. inc, 2022;
Guru Saham, 2022; Indonesia Stock Exchange, 2022; Kuliah Saham, 2022; Nasahind, 2022;
Ngerti Saham, 2022; Ruang Saham, 2022; Saham Daily, 2022; Saham Rakyat, 2022; Saham Talk, 2022; Sahamology, 2022; Stock Up Indonesia, 2022; Stockbit, 2022).
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contrast to the Rp1.32 billion-worth loss recorded in June 2020. “X’s” book value per share was Rp1.160. With “X’s” current stock price, in conjunction with Indonesia’s infrastructure as a positive catalyst and “X’s” fundamental outlook to gradually improve its balance condition, “X’s” stocks will therefore be of interest to be collected.
Posts with negative sentiments were coded -1. These posts included complaints, sarcasm, criticisms, and reflections on negative emotions such as anger, annoyance, and disappointment. A post would be categorized as negative if at least 2/3 of the whole sentences in the post were filled with negative sentiments (Wei et al., 2017). An example of a post with a negative sentiment is as follows: In 2020, the income of the issuing tycoon duo TP Rachmat and Boy Thohir plunged by 20.97% (yoy) to US$175.51 million.
This free fall occurred in all segments, whether in the ammonia, LPG sales, or processing service business sector. The selling expense rose to 769.2% (yoy) from US$2.23 million to US$257.15 million. "X" must incur a net loss of the current year that could be attributed to the parent entity owner at US$19.12 million, although it managed to record a net profit of US$2.63 million in 2019. In terms of stock price movement, X's MTD stock price had fallen by 44%.
Meanwhile, posts with neutral sentiments were coded 0. These posts were posts that were neither positive nor negative. An example is as follows: PT X was initially focused on the information technology sector but later shifted its business focus to investing in several companies, including Indomaret, KFC Indonesia, and Sari Roti.
Although during the COVID-19 pandemic, the company incurred sizeable losses in several business aspects, the company managed to record income increases and generate net profits. In 1996, PT X officially joined the Internet Service Provider (ISP) group under the business brand DNET. In 2007, the company was taken over by PT Y, which continued its business in the information technology field.
The researcher developed a codebook for an easier coding procedure. Coding reliability tests using single independent coding were carried out by giving 20% of the sample along with the developed codebook (Maiorescu, 2017). The reliability tests were performed to compare and see the consistencies of two codings in generating values using Kappa. A score of 0.57 on the first reliability test failed to meet the
401 minimum intercoder reliability score, which is 0.82 (Maiorescu, 2017), so the difference in the coding number was discussed and clarified further. In the second reliability test, a score of 0.925 successfully met the minimum intercoder reliability score. Therefore, the researcher believed that Instagram post coding had a high level of reliability.
Abnormal returns were calculated during a 100 trading days observation estimation period as this period was considered sufficient to accurately estimate the alpha and beta values (Armitage, 1995). To minimize the event effect, the estimation period started on t-11 and ended on t-110 (Fan et al., 2020a). The window period was determined as three days pre-event and three days post-event to avoid confounding effects (Sprenger et al., 2014). In addition, there is a possibility that the market will only react to the information if the window period is determined to be at least seven days (Dewi & Sukartha, 2015).
The date on which the post was made would be considered the event date. If the post were made on a non-business day, the following day would be considered the event date because investors can react to information during this period (Brans & Scholtens, 2020). Before calculating the abnormal return in the window period, the stock return in the estimation period was first calculated using the following equation:
𝑅𝑖,𝑡 = ln 𝑃𝑖,𝑡
𝑃𝑖,𝑡−1 (1)
where 𝑅𝑖,𝑡 is the i stock return in the period t, 𝑃𝑖,𝑡 is the i stock price in the period t, and 𝑃𝑖,𝑡−1 is the i stock price in the period t-1. Besides, it was also considered necessary to calculate the market return during the estimation period using the following equation:
𝑅𝑀𝑡 = ln 𝑃𝐼𝑡
𝑃𝐼𝑡−1 (2)
where 𝑅𝑀𝑡 is the market return in the period t, 𝑃𝐼𝑡 is the IDX Composite value in the period t, and 𝑃𝐼𝑡−1 is the IDX Composite value in the period t-1. Then, regression was performed on 𝑅𝑖,𝑡, and 𝑅𝑀𝑡 data to find the alpha and beta values. Previously, the data were analyzed using classical assumption testing to meet the regression test prerequisites (Hair et al., 2010), which included normality, heteroscedasticity, and autocorrelation tests. The normality test, conducted using a Kolmogorov-Smirnov test,
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yielded a p-value < 0.05, meaning that the data were not normally distributed. The heteroscedasticity test was performed using a Breusch-Pagan test, and a p-value < 0.05 suggested the presence of heteroscedasticity. Lastly, the autocorrelation test was carried out using a Wooldridge test4, and it was found that autocorrelation was present, given that the p-value obtained was lower than 0.05. Therefore, the regression test was performed using the Generalized Least Squares (GLS) model to deal with the time - series and cross-sectional autocorrelation problem and the homoscedasticity assumption violation to produce more stable and efficient estimates (Kosmaryati et al., 2019).
GLS regression was conducted using the data during the estimation period using the equation below to obtain the alpha and beta values:
𝑅𝑖,𝑡 = 𝛼 + 𝛽𝑖𝑅𝑀𝑡+ 𝜀𝑖,𝑡 (3)
where 𝛼 is the intercept for securities, 𝛽𝑖 is a slope coefficient, which is the beta of the security i, and 𝜀𝑖𝑡 denotes an error. In equation 3, variable Y, whose more than 50%
of the data were scored 0, was removed (n = 159) (Hair et al., 2019). Data that could not be estimated for having a constant or flat pattern (n = 56) were removed, leaving a total of 2,675 event data. Upon finding the alpha and beta values, the expected return (𝐸(R𝑖,𝑡)) of each stock was calculated using the market model (Henderson, 1990). The market model is a generic estimation technique used in event studies (Hu, 2018). This model can control the entire market's movement during the event and post-event periods and calculate each stock's risk level (Werner, 2010). The calculation of the expected return was conducted using the equation below:
𝐸 (𝑅𝑖,𝑡) = α + 𝛽𝑖𝑅𝑀𝑡 (4)
The next step was calculating the abnormal return (𝐴𝑅𝑖,𝑡) in the window period using the equation below:
𝐴𝑅𝑖,𝑡 = 𝑅𝑖,𝑡 − 𝐸 (𝑅𝑖,𝑡) (5)
Afterward, the data were analyzed using a multiple regression test to determine the effect of stock information over social media on abnormal returns. The data were first analyzed using classical assumption tests to meet the regression test prerequisites (Hair et al., 2010), which included normality, heteroscedasticity, multicollinearity, and
403 autocorrelation tests. The normality test was performed using a Saphiro-Wilk test, from which a p-value < 0.05 was obtained, meaning that the data were not normally distributed. The heteroscedasticity test was performed using a Breusch-Pagan test, where heteroscedasticity was found to be present, with a p-value < 0.05. Meanwhile, the autocorrelation test was conducted using a Wooldridge test, from which autocorrelation was found with a p-value < 0.05. Lastly, the multicollinearity test was performed with pairwise correlation, where multicollinearity between variables was found. Therefore, the regression test was carried out using the Generalized Least Squares (GLS) model. Therefore, the regression test was carried out using the Generalized Least Squares (GLS) model. GLS produces more stable and efficient estimates than OLS (Iswati et al., 2014) due to its ability to overcome the problems of time series and cross-sectional autocorrelation and violation of homoscedastic assumptions (Kosmaryati et al., 2019).
Next, the hypothesis was tested using Generalized Least Squares (GLS). The regression model was formulated to be as follows:
AR = 𝛽𝑜 + 𝛽1S + 𝛽2lnSize + 𝛽3lnSpread + 𝛽4I + 𝛽5CA + 𝛽6Rm + e (6) Where:
This study uses control variables to determine the contribution of the independent variable to the dependent after accounting for other factors (Carlson & Wu, 2012) to obtain reliable results (Callan & Thomas, 2009). To control the stock price trend, the market return on the event day (Rm) was added as a control variable (Ajjoub et al., 2021).
Market capitalization (lnSize) and bid-ask spread (lnSpread) were used to control company size and company liquidity (Gu & Kurov, 2020). Industry type (I) was also added as a control variable as high-risk industries typically pay great amounts of
AR = Abnormal return 𝛽𝑜 = Constant variable 𝛽1−6 = Variable coefficients
S = sentiment regarding stock news lnSize = Natural logarithm of market
capitalization
lnSpread = Natural logarithm of a bid-ask spread
I = Industry
CA = Corporate action
Rm = Natural logarithm of market
return e = Error
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compensation (high-risk, high return) (Suhardjanto & Nugraheni, 2012). Corporation action was also added to control corporate actions. Table 2 explains the variables, definitions, and research data sources used in greater detail.
Table 2
Variable Operational Definition
Variable Definition Data
Sources Dependent Variable
AR An abnormal return was obtained from the difference between the actual and expected returns in the window period.
Author calculation
Independent Variable
S Posts Sentiment Instagram
Control Variables
lnSize Natural logarithm of market capitalization Eikon Refinitive Database lnSpread Natural logarithm of the bid-ask spread Eikon
Refinitive Database I Dummy variables 1-11 to classify the type of
industry.
www.idx.co.id CA Dummy variable 1 for the day the corporate
action occurred and 0 otherwise
Eikon Refinitive Database
Rm Natural logarithm of market returns. Eikon
Refinitive Database
4. Results and Discussion
Table 3 provides information on number of posts by industry type. Stocks in the financial industry were the most talked about stocks among stock news accounts. This is because the financial industry was one of the industries with the most number of issuers on the IDX (n = 105) (IDX, 2021). In 2021, the stocks in the financial sector generated positive performance thanks to the drive from the Financial Services
405 Authority’s policy2 on digital banking (CNBC Indonesia, 2021d). This policy has attracted many global investors to invest in digital banks, drastically leading up the stock prices in the banking sector (CNBC Indonesia, 2021d, 2021a). Besides, the financial industry was a sector with the largest market capitalization on the IDX (IDX, 2021), hence strongly favored by investors (Utami, 2021). The considerable interest in the financial sector is evident in the higher trading volume than in others (IDX, 2021).
On the other hand, the transportation and logistics industry was least discussed by stock news accounts. This is because this industry only had 28 issuers, the least number of issuers in all industries (IDX, 2021). Stocks in the transportation and logistics industry attracted minimal interest from investors, as can be seen from the smallest trading volume in all industries (IDX, 2021).
Table 2.
Total posts and Industry Type
Industry Type Total
Posts %
Positive Sentiment
Neutral Sentiment
Negative Sentiment
Total % Total % Total %
Financials 636 24% 384 60% 175 28% 77 12%
Consumer Non- Cyclicals
448 17% 220 49% 148 33% 80 18%
Basic Materials 287 11% 159 55% 84 29% 44 15%
Infrastructures 275 10% 144 52% 105 38% 26 9%
Energy 262 10% 159 61% 69 26% 34 13%
Consumer Cyclicals
248 9% 144 58% 82 33% 22 9%
Healthcare 153 6% 88 58% 48 31% 17 11%
Industrials 137 5% 75 55% 48 35% 14 10%
Properties & Real Estates
103 4% 52 50% 41 40% 10 10%
Technology 81 3% 56 69% 21 26% 4 5%
Transportation &
Logistic
45 2% 31 69% 10 22% 4 9%
Total 2675 1512 57% 831 31% 332 12%
2 Regulation of the Financial Services Authority No. 12/POJK.03/2021 on Commercial Banks regulates the requirements for establishing a new bank, the operating aspect, business process, including digital services or digital bank establishment, and business termination. Re gulation of the Financial Services Authority No. 13/POJK.03/2021 regulates reinforcement in bank product licensing and organizing (Otoritas Jasa Keuangan, 2021)
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Table 3 also informs on the number of sentiments earned by each industry. The stocks most frequently received positive sentiments in the technology and transportation industries. The stocks in the technology industry grew rapidly over the year 2021, reaching 707.56% (Kontan, 2021a). The COVID-19 pandemic in 2021 served as a positive catalyst for companies engaged in the technology sector, given society's increased digital technology use (Rahardika et al., 2022). Issues regarding Gojek and Tokopedia (GoTo) IPO also drove the positive sentiments received by stocks in the technology sector (Kontan, 2022). The transportation industry also saw an improved performance from the increase in the number of passengers across various modes of transportation (air, land, and sea) after recovering from the pandemic impact (Katadata, 2021). On the other side, the stocks most often receiving negative sentiments were those in the non-cyclical consumer industry. This is due to society's low purchasing power and policy changes, such as the rise of tax rates for cigarettes and sweetened drinks (Kontan, 2021b), which eventually caused the stocks in the non-cyclical consumer sector to record a negative performance down to -16.04% (Kontan, 2021a).
Table 4 also shows that the mean abnormal return (AR) was positive. This positive value indicated that the stock return was higher than expected. This is because the stocks were assessed to be higher than their intrinsic values. Panel A suggests that the stocks talked about by stock news accounts on Instagram had a mean abnormal return that was higher than the mean market return, meaning that the performance of those stocks was better than their market performance.
Table 4 shows that the mean abnormal return generated during the event period was higher than the mean abnormal returns generated during the event window and the estimation window. Proven by the Wilcoxon Signed-Rank Test results, this held for the abnormal return medians that resulted between the event period and the event window (Z = 5.224, p < 0.001) and the estimation window (Z = 3.077, p < 0.001). The abnormal return produced in the event window was higher than that in the estimation window, but there was no significant difference in the median (Z = 0.134, p < 0.05). This reflects that information on social media triggered exaggerated responses from investors, which resulted in a fleeting impact that lasted only during the event period. This is consistent
407 with prior research, which found that the impact of information over social media on stock returns was temporary in nature (Gu & Kurov, 2020; Teti et al., 2019). A possible misinterpretation might explain by investors (Gao, 2018) or by impostor involvement.
Therefore, the stock price during the event window became lower when investors realized their mistaken interpretation (Gao, 2018).
Table 4.
Descriptive Statistics
Variable Obs Mean Std.
Dev.
Min Max
Panel A: Event Period
AR 2675 0.009 0.053 -0.282 0.362
lnSize 2675 16.690 1.984 10.350 20.658
lnSpread 2675 -0.019 0.195 -2.593 0.240
Rm 2675 0.001 0.009 -0.021 0.034
Panel B: Event Window Period
AR 18725 0.002 0.040 -0.107 0.362
lnSize 18725 16.788 1.952 10.350 20.668
lnSpread 18725 -0.018 0.198 -2.757 0.219
Rm 18725 0.000 0.008 -0.021 0.034
Panel C: Estimation Window Period
AR 267500 0.001 0.035 -0.155 0.367
lnSize 267500 16.625 2.102 0.000 20.668
lnSpread 267500 -0.014 0.190 -3.008 0.312
Rm 267500 0.001 0.010 -0.051 0.034
By sentiment, the abnormal return medians between positive and neutral sentiments (Z = 3.934, p < 0.001) and between positive and negative sentiments (Z = 8.171, p < 0.001)8 were also different, proven by Wilcoxon Rank Sum Test. The same was true between neutral and negative sentiments (Z = 5.364, p < 0.001). Posts with positive and neutral sentiments generated positive abnormal returns. This shows that positive and neutral sentiments could cause stock value overestimation relative to the intrinsic value. On average, the abnormal return generated by posts with positive sentiments (X̅ = 0.015) was higher than the mean abnormal return generated by posts with neutral sentiments (X̅ = 0.007). Posts with positive sentiments contained good news, where stocks were believed to have profitable performance in the future. The
408
good news would attract investors to make purchase decisions, eventually lifting the stock prices (Sul et al., 2017).
On the other hand, posts with negative sentiments resulted in negative abnormal returns (X̅ = -0.007). Negative sentiments could lead to selling decisions and lower stock prices (Sul et al., 2017). This shows that negative social media sentiments are considered bad news that would draw negative responses from the market.
The mean lnSize value presented in Table 4 shows that the stocks talked about by stock news accounts were stocks with a high market capitalization (over 10 billion) as they were attractive to investors (Utami, 2021). Typically, stocks featured in stock news accounts' contents on Instagram were stocks with high liquidity, as seen from the negative lnSpread value. The negative lnSpread value marked that the stocks had more offering than demand. This shows that purchasers valued the stocks lower than the value offered by sellers.
Table 5 presents the results of the correlation test between variables. It was found that sentiments (S) and market returns (Rm) were positively correlated with abnormal returns (AR). This shows that the better the sentiment circulating over social media, the higher the abnormal return. This finding is consistent with the results of the previously conducted discrimination test. In addition, market returns also increased with the increase in positive sentiments circulating on social media.
Nonetheless, bid-ask spread (lnSpread) was negatively correlated with abnormal returns (AR). The negative relationship between lnSpread and AR suggests that the higher the offering, the lower the abnormal return generated. This is because the higher the offering, the higher the nominal the company must incur to purchase stock;
therefore, the abnormal return would be lower. This also shows that the more liquid the stock, the higher the abnormal return generated. There was also a negative correlation between market capitalization (lnSize) and abnormal returns (AR), meaning that the larger the company, the lower the abnormal return that would be generated. The explanation would be that big caps tend to have numerous alternative information sources than small caps (Bartov et al., 2018). Thus, information on big caps did not come as too much of a surprise for investors, leading to low abnormal return generation.
409 Table 5.
Pairwise Correlation Results
Variables (1) (2) (3) (4) (5) (6) (7)
(1) AR 1.000
(2) S 0.144*** 1.000
(3) lnSize -0.104*** 0.016 1.000
(4) lnSpread -0.131*** -0.005 0.021 1.000
(5) I 0.002 0.046* 0.042* -0.104*** 1.000
(6) CA 0.003 0.019 0.084*** 0.011 0.026 1.000
(7) Rm 0.063** 0.126*** -0.041* -0.014 -0.014 0.026 1.000 Note: *** p<0.001, ** p<0.01, *p<0.05
Tables 6 and 7 provide the GLS regression results of all research variables.
Sentiments on social media (S) positively affected abnormal returns (AR). Therefore, the research hypothesis (H1) could not be rejected. This finding parallels previous studies, which found that sentiments on social media positively affected stock returns (Agarwal et al., 2021; Fan et al., 2020a; Gu & Kurov, 2020). Social media contains relevant information (Gu & Kurov, 2020) and, therefore, could increase stock returns beyond the expected return. This reflects that sentiments on social media could increase a stock's value over its intrinsic value. The influence that was exerted by social media (S) on abnormal returns (AR) also suggests that information on a company on social media played a significant role in shaping public opinions and in influencing market responses (Bartov et al., 2018; Blankespoor, 2018; Bollen et al., 2011; Kietzmann et al., 2011).
Information on a company may come from two sources: from within the company itself and from non-company users, such as social media users (Lei et al., 2019; Zhou et al., 2015). The information that comes from within the company can be misleading to the information users (Lei et al., 2019) since it tends to be biased, with only positive news or information being published (Yang & Liu, 2017). On the other hand, investors may verify the information from the company using the information from non- company users while at the same time sharing their opinions with those users (Lei et al., 2019). In this case, the investors use the information they derive from stock news accounts for verification and share the information with other users.
410 Table 6
GLS Regression Results
Variables Model 1 Model 2 Model 3
Coef. p-value Coef. p-value Coef. p-value
S 0.0103 0.0000*** 0.0100 0.0000***
lnSize -0.0025 0.0000*** -0.0026 0.0000***
lnSpread -0.0322 0.0000*** -0.0322 0.0000***
Rm 0.3335 0.5510* 0.2296 0.0380*
I 0.0001 -0.0002 0.4850
CA 0.0065 0.0055 0.5970
Constant 0.0523 0.0000*** 0.0052 0.0000*** 0.0497 0.0000***
Wald- Chi2
84.8100 0.0000*** 56.6000 0.0000*** 141.0300 0.0000***
AIC -8497.3026 -8477.8217 -8549.2490
BIC -8461.9524 -8466.0383 -8508.0071
Note: ***p<.001, *p<0.05
The influence of sentiments on social media (S) on abnormal returns (AR) indicates that social media has been in use to inform investors' investment decision- making (Kadous & Mercer, 2017). Investors use social media because they face limited access to professional data sources such as Bloomberg and Thomson Reuters (Bukovina, 2016). As a result, social media platforms such as Instagram serve as an alternative for investors to communicate with other users at a minimum cost (Lei et al., 2019). Through social media, investors may share information using features like share and comment (Bukovina, 2016), which are helpful for them to devise investment strategies following recent news and trends (Agarwal et al., 2019).
The fact that investors make decisions based on information that they obtain from social media shows that investors are not perfectly rational. This irrational behavior of the investors is explainable by the behavioral finance concept. The behavioral finance concept assumes that stock prices are influenced by investors' irrational behavior that relies on sentiments and other non-fundamental information (Ge et al., 2020). In this context, investors depend on the information circulates on social media to make investment decisions. Irrational decision-making by investors stems from their inability to collect and process information from social media (Li et al., 2019) and their falling under the influence of others' perspectives (Zahera & Bansal, 2018).
411 Among the stocks that generated abnormal returns from information on social media were those of PT Bank Capital Indonesia Tbk (BACA). The account emtrade_id presented information on BACA's rising stock price following an issue with BACA's partnership with OVO and Grab. On the same day, BACA's stock price rose by 22%
from 432 to 530. This increase in BACA's stock price, in turn, led to abnormal return generation at a rate of 0.225. However, the impact of the information lasted only on the day the information was posted, given that the stock price went back down by 6% on the following day. Correspondingly, the abnormal return generated also declined to - 0.060.
The results showed that the sentiment coefficient (S) is higher in Model 3 (0.010) than in Model 2 (0.001). This indicates that the contribution of social media sentiment (S) on abnormal returns (AR) increases when the control variables are considered (Carlson & Wu, 2012). Therefore, the results of this study are reliable (Callan &
Thomas, 2009). The higher the log-likelihood, the higher the fit between the model and the data used (Loog & Jensen, 2015). Therefore, Model 3, with the lowest AIC and BIC values, is the model with the best fit (Schermer & Martin, 2019). According to Table 6 Model 3, the control variable lnSize (market capitalization) has a negative effect on abnormal returns. This shows that the higher the company's market capitalization, the higher the abnormal return will be. This finding is in line with the research by Bartov et al. (2018) and Gu & Kurov (2020), which found that the impact of information on social media on abnormal returns was more substantial in small caps. This is because small caps do not have that many alternative information sources and because they probably have yet to reach the market, resulting in abnormal returns (Bartov et al., 2018).
Meanwhile, big caps require considerable transaction volume and value to shift stock prices (Werastuti, 2012). Besides, big hats are in strong information environments, so they tend to have multiple alternative information sources (Bartov et al., 2018). Most likely, the company's information has spread through other sources. This finding shows that social media could be a relevant information channel for investors.
This research discovered that the control variable bid-ask spread (lnSpread) negatively affected abnormal returns (Model 3). This is because the higher the offering,
412
the higher the nominal the company must incur to purchase stock; therefore, the abnormal return would be lower. This shows that sentiment (S) associated with liquid stocks generates higher abnormal returns (AR) and vice versa. This study also found that market return (Rm) did not affect abnormal return (AR) (Model 3). These results indicate that the increase or decrease in the IHSG's value does not affect the abnormal returns generated by the company.
Meanwhile, the variable industry (I) did not significantly affect abnormal returns (Table 6 Model 3). However, since this variable was a dummy variable, further analysis of the effect of each industry on abnormal returns is necessary. Of the existing 11 industries, sentiment on social media only positively affected stocks engaged in the energy, financial, healthcare, and basic materials industries. This is because the financial and energy industries were more sensitive to news reporting (Lee, 2020). This finding is in contrast to the research by Agarwal et al. (2021), which found that information on social media only significantly affected stock returns in the financial industry. In the case of stocks in the non-cyclical consumer, industrial, infrastructure, cyclical consumer, transportation and logistics, property and real estate, and technology industries, information on social media did not significantly affect abnormal returns.
Table 6 Model 3 shows that the variable corporate actions (CA) did not significantly affect abnormal returns. This demonstrates that concurrent social media sentiment and corporate actions do not affect the abnormal returns generated by the company. This is in line with prior research, which found that corporate actions did not significantly affect abnormal returns (Sululing et al., 2022). This is because positive information on things such as corporate actions is often leaked before the official publication of the information by the company (Sprenger et al., 2014; Sululing et al., 2022). That there were many factors affecting abnormal returns shows that stock prices were influenced by fundamental information and sentiments on social media.
413 5. Conclusion, Implication, and Limitations
5.1. Conclusion
This research sought to determine the effect of stock news or information on Instagram on abnormal stock returns. It was then found that information on social media positively affected abnormal returns. This shows that investors also considered information circulated on social media in making investment decisions. In other words, investors were not perfectly rational in making decisions as they were also influenced by psychological factors and other people's perspectives. The effect of information on social media on abnormal returns was only temporary as there remained possible misinterpretation on the investors' part and impostor involvement.
5.2. Implication and Limitation
This research contributes to the behavioral finance literature by investigating the effect of stock information on social media that was posted by stock news accounts on Instagram on abnormal returns. In addition, investors can take advantage of the information on social media by selling their shares when a stock receives positive sentiment to obtain abnormal returns. When sentiment towards a stock is negative, investors can purchase it because the stock price is below its intrinsic value and therefore has the potential to rise. This research is limited to the Instagram platform.
Therefore, further research is expected to examine the effect of sentiment on Tik Tok or Youtube on stock return.
Acknowledgment
This work is supported by the Internal Research Grant, Universitas Negeri Malang No.
19.5.441/UN32.20.1/LT/2022.
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